Modern computer systems may be required to execute multiple machine learning processes simultaneously and in real-time. Such systems often rely upon streaming data as a real-time training input to the machine learning processes, in which data that is located initially outside the system streams continuously into the system. As long as system processing capacity meets or exceeds the demands created by the streaming data and the tasks being performed by the system, the system performs properly. However, if the demands so created exceed the processing capacity of the system, the continuation of in-streaming data will cause the system to stall, or will lead to the loss of critical data, or will create other problems of system performance. It is known that machine learning processes often require a training of multiple mathematical models. It is also know that such training can create enormous variability in processing demand, perhaps as much as two orders of magnitude, or even more, from a base case. Systems and methods are required for meeting in real-time the great variability of processing demand in such cases.